CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks
One of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacit...
Autores Principales: | Pinzón Trejos, Cristian, Herrero, Álvaro, De Paz, Juan, Corchado, Emilio, Bajo, Javier |
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2018
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http://ridda2.utp.ac.pa/handle/123456789/4783 |
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RepoUTP47832021-07-06T15:35:05Z CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks Pinzón Trejos, Cristian Herrero, Álvaro De Paz, Juan Corchado, Emilio Bajo, Javier SQL Injection Intrusion Detection CBR SVM Neural Networks SQL Injection Intrusion Detection CBR SVM Neural Networks One of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacity for the classification of malicious codes. The agent also incorporates advanced algorithms in the reasoning cycle stages. The reuse phase uses an innovative classification model based on a mixture of a neuronal network together with a Support Vector Machine in order to classify the received SQL queries in the most reliable way. Finally, a visualisation neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The Classifier Agent was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented here. One of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacity for the classification of malicious codes. The agent also incorporates advanced algorithms in the reasoning cycle stages. The reuse phase uses an innovative classification model based on a mixture of a neuronal network together with a Support Vector Machine in order to classify the received SQL queries in the most reliable way. Finally, a visualisation neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The Classifier Agent was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented here. 2018-06-05T19:20:00Z 2018-06-05T19:20:00Z 06/23/2010 06/23/2010 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://ridda2.utp.ac.pa/handle/123456789/4783 eng eng https://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess application/pdf application/pdf |
institution |
Universidad Tecnológica de Panamá |
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Repositorio UTP – Ridda2 |
language |
Inglés Inglés |
topic |
SQL Injection Intrusion Detection CBR SVM Neural Networks SQL Injection Intrusion Detection CBR SVM Neural Networks |
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SQL Injection Intrusion Detection CBR SVM Neural Networks SQL Injection Intrusion Detection CBR SVM Neural Networks Pinzón Trejos, Cristian Herrero, Álvaro De Paz, Juan Corchado, Emilio Bajo, Javier CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks |
description |
One of the most serious security threats to recently deployed databases has been the SQL Injection attack. This paper presents an agent specialised in the detection of SQL injection attacks. The agent incorporates a Case-Based Reasoning engine which is equipped with a learning and adaptation capacity for the classification of malicious codes. The agent also incorporates advanced algorithms in the reasoning cycle stages. The reuse phase uses an innovative classification model based on a mixture of a neuronal network together with a Support Vector Machine in order to classify the received SQL queries in the most reliable way. Finally, a visualisation neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The Classifier Agent was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented here. |
format |
Artículo |
author |
Pinzón Trejos, Cristian Herrero, Álvaro De Paz, Juan Corchado, Emilio Bajo, Javier |
author_sort |
Pinzón Trejos, Cristian |
title |
CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks |
title_short |
CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks |
title_full |
CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks |
title_fullStr |
CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks |
title_full_unstemmed |
CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks |
title_sort |
cbrid4sql: a cbr intrusion detector for sql injection attacks |
publishDate |
2018 |
url |
http://ridda2.utp.ac.pa/handle/123456789/4783 |
_version_ |
1796209472171933696 |
score |
12.041657 |